A Comparative Analysis for AI Developers and Data Scientists
As businesses clamor for greater artificial intelligence (AI) applications, the race is on to produce quicker and more efficient hardware components that can support those workloads. The commonly discussed options in AI hardware are GPUs and TPUs. GPUs and TPUs are therefore specialized computing resources, however for different stages of the AI pipeline. For developers, data scientists, and businesses looking to leverage AI capabilities, knowledge of the strengths and weaknesses of each is critical in getting optimal performance from your job.
We take an in-depth look at how GPUs compare and differ with TPUs illustrated through factors of compute, price points & scalability along with their application across various AI workloads.
What Are GPUs and TPUs?
GPUs: The Workhorse of AI Computing
While GPUs were initially created for gaming and graphics applications to render images and video, they have now become an essential element in high-performance computing (HPC), particularly for AI/deep learning tasks. GPUs are specialized, highly parallel processors manufactured by companies like NVIDIA and AMD that can execute thousands of threads concurrently which suits well with data-intensive tasks such as training neural networks.
GPUs are good at general-purpose AI types of workloads and in particular deep learning. Thanks to their ability to process huge amounts of data in parallel, they can learn much bigger models with more complex matrix multiplications than before and can better deal with massive unstructured datasets.
TPUs: The AI-Specific Accelerator
Google also developed Tensor Processing Units (TPUs) for AI workloads. While GPUs were modified to work with AI, TPUs are machine-learning-specific chips and intended for deep learning works most notably. TPUs are a type of specialized hardware that has been created to work specifically with TensorFlow, one of the most powerful and popular machine learning frameworks.
While TPUs are not as general-purpose as the GPUs used in standard gaming consoles, they have been optimized for specific AI tasks (especially tensor algebra) and so can provide dramatic improvements in performance for applications such as natural language processing (NLP), image classification/recognition, etc.
Performance Comparison: GPU vs. TPU
Raw Processing Power
While GPUs have traditionally led the charge in terms of raw processing power. The high-end GPUs, such as the NVIDIA A100 or AMD Instinct series offer incredible computational capabilities with tens of thousands of cores that are designed specifically for parallel execution. These GPUs can be used to train and infer a diverse set of AI models, they also get accelerated in many machine learning frameworks such as TensorFlow, PyTorch, or Caffe.
HOWEVER, TPUs give massive efficiency gains in very specific AI tasks. Google’s TPU v4, for instance, can provide up to 275 teraflops of compute performance that is faster than some top-end GPUs on specific deep learning workloads. The main difference is in the architecture: TPUs are created to run tensor-based computations, which are a huge part of many deep-learning operations. While TPUs can be orders of magnitude faster than GPUs for tasks like matrix multiplication, that performance only comes from their specialization.
Speed in AI Model Training
AI-model training for example, when you train a large neural net; since AI models are usually very complex the needed compute resource could be hours to days of run time. GPUs have been the obvious choice because of their outstanding parallelism, but TPUs are catching up, especially for TensorFlow projects.
For example, TPUs have been able to learn large-scale natural language processing algorithms like the BERT and GPT substantially faster in benchmark tests. Through a TPU pod, which is an interconnected web of TPUs you can perform the training in some fraction of time to what it would otherwise take on a GPU-based system. That said, for smaller projects or if you’re working with something that isn’t TensorFlow-based (more on this in the next section), GPUs are a more general-purpose choice.
Cost-Effectiveness: Which Offers Better Value?
GPUs: High Initial Cost, Wide Availability
In practice, most AI developers write their work for GPUs since they are ubiquitous and have a rich ecosystem of support to help them explicitly program these devices. But, keep in mind that high-end GPUs are not cheap. The NVIDIA A100, for instance, retails at over $12K daily making it a very expensive proposition for solo developers or small businesses. However, as GPUs are becoming mainstream today, a lot of farms or cloud providers offer cheap GPU-based solutions by which developers can access high-end hardware without paying the prominent cost upfront.
TPUs: A Cloud-Based Option with Specialized Cost Benefits
TPUs, being available primarily as a cloud service on Google Cloud are more economical for developers who require massive scaling but would rather not commit to buying hardware. They usually charge by the usage to use TPU-based services, which enables you can save a lot of money if your business requires only deep learning service in certain conditions. Still, TPUs shine in TF environments due to their cost structure. These are less useful when using anything else, which could mean more cost for developers having to re-architect workflow or change frameworks just to get the TPU specifics right.
Flexibility: Which is More Adaptable to Various AI Workloads?
GPUs: The All-Purpose Solution
Flexibility has been one of the main keynotes for GPUs. They do not belong to a particular framework or use case and are usable in the entire spectrum of AI workloads. From training convolutional neural networks (CNNs) on images, processing sequences for time-series analysis, or running reinforcement learning solutions; GPUs have you covered. This flexibility also makes them a popular choice among AI researchers and developers moving between different projects requiring an out-of-the-box solution.
TPUs: Exceptional at Specialized Tasks
On the flip side, TPUs are much more rigid in comparison. Their architecture is ideal for TensorFlow and especially well-suited to some neural network types (dense, feedforward networks; also great at convolutional). They are very good at this but rigid and inflexible when it comes to other machine learning frameworks or esoteric AI architectures. However, for developers using a framework like PyTorch, this could lead to considerable code churn a potential barrier to adoption.
Ease of Use: Developer Experience Matters
GPUs: A Larger Community and Resource Pool
GPUs have a healthy ecosystem, good documentation and support, also community resources done right. CUDA from NVIDIA has become the de facto standard for GPU programming there are a ton of libraries and tools available to optimize AI workload. All of this means that developers have more available than ever to get off the ground with GPU computing while having become better equipped to diagnose any problems they might face.
TPUs: Optimized for TensorFlow, Limited Elsewhere
Though TPUs are custom-designed to run TensorFlow, which is a plus for TensorFlow devs. Google has more documentation, tools, and the like for TPUs than most other providers do at all; but in comparison to GPUs not so much. For developers who may be new to TensorFlow or just starting with TPU programming, this could lead to a longer setup time.
Conclusion: GPU or TPU – Which Should You Choose?
Whether to use GPUs or TPUs depends on your specific requirements for your AI project. GPUs continue to be the most versatile and supported processors with a large performance advantage on many AI workloads and frameworks. Perfect for developers looking to be able to develop without worrying, with a lot of community support and the all-round solution.
For TensorFlow users working on large-scale deep learning projects TPUs are a game-changer. Although they have been more specialized in their application, a large selection of newer AI chips also offer high speeds and efficiency with particular workloads making them ideal for businesses that are ready to scale up performance.
In conclusion, GPUs are still the king when it comes to general AI development. But then, again Zoe also points out that if your project has some strong deep learning focus or is TensorFlow heavy, TPUs can provide a pretty big jump in performance.
